Polymarket Whale Tracker

Track whale traders, decode smart money flows, and get computed analytics signals from Polymarket prediction markets — all in one API call.

Stop guessing what sophisticated traders know. This Actor cross-references three Polymarket APIs (Gamma + Data + CLOB) to deliver whale tracking, position analytics, and market health signals that are not available from any single Polymarket endpoint.

What This Actor Does

This Actor connects to three Polymarket public APIs simultaneously:

• Gamma API — Event metadata, market listings, pricing data
• Data API — Holder positions, trade history, leaderboards
• CLOB API — Live orderbook depth, midpoint pricing, spread analysis

It then computes derived analytics — Smart Money Consensus, Whale Sentiment, Market Health Score, Conviction Score, and more — and delivers structured JSON ready for trading decisions, research pipelines, or AI agent workflows.

Why This Actor Is Different

Feature	Typical Polymarket Scraper	Whale Tracker
Data source	Single API	3 APIs cross-referenced
Output	Raw JSON dump	Computed actionable signals
Whale identification	None	Tiered system (Whale / Shark / Dolphin / Fish / Retail)
Analytics	None	Market Health, Whale Sentiment, Trade Flow analysis
Orderbook analysis	None	Depth analysis + $10K/$50K/$100K impact simulation
ML analysis	None	Anomaly detection, whale clustering, trend analysis, NLG reports
Error resilience	Crashes on API errors	Graceful fallback per market

---

Four Analysis Modes

1. Market Analysis (market_analysis)

Deep analysis of specific prediction markets. Provide event slugs (from the Polymarket URL after /event/) and get whale breakdown, trade flow, sentiment metrics, orderbook depth, and impact simulation for each market within that event.

Best for: Researching a specific market before placing a position.

Cost: $0.15 per market

2. Whale Profile (whale_profile)

Complete wallet intelligence for any trader address. Returns all active positions, historical P&L, win rate, ROI, and position breakdown across markets.

Best for: Evaluating whether a specific whale trader is worth following.

Cost: $0.25 per wallet

3. Bulk Scan (bulk_scan)

Batch scan of the top N active Polymarket markets by 24-hour volume. Returns Market Health Score, current price, volume, and top holder count for each market. Fast way to identify where activity is concentrated.

Best for: Daily monitoring, finding active markets, feeding data pipelines.

Cost: $1.50 per scan

4. Alert Check (alert_check)

Lightweight change detection. Checks for large trades above your threshold on specific markets. Designed to run on a schedule (e.g., hourly via Apify Schedule) for continuous monitoring at minimal cost.

Best for: Automated whale monitoring with webhook notifications.

Cost: $0.05 per check

---

Input Parameters

Parameter	Type	Default	Required	Description
mode	string	market_analysis	Yes	Analysis mode: market_analysis, whale_profile, bulk_scan, alert_check
eventSlugs	string[]	[]	For market_analysis	Event slugs from Polymarket URL (e.g., presidential-election-winner-2028)
marketConditionIds	string[]	[]	For alert_check	Hex condition IDs for markets to monitor
walletAddresses	string[]	[]	For whale_profile	Wallet addresses to profile (e.g., 0xd04d93...)
category	string	all	No	Category filter for bulk_scan: politics, crypto, sports, pop-culture, business, science
whaleThreshold	integer	50000	No	Minimum USD position to classify as whale ($1K–$10M)
tradeAlertThreshold	integer	10000	No	Minimum USD trade size to flag as "large trade" ($1K–$1M)
timeWindow	string	24h	No	Analysis lookback window: 1h, 6h, 24h, 7d, 30d
includeOrderbook	boolean	true	No	Include CLOB orderbook depth and impact simulation
includeTradeFlow	boolean	true	No	Include individual trade-level flow analysis
topHoldersCount	integer	20	No	Number of top holders to analyze per market (5–100)
maxMarkets	integer	20	No	Number of markets to scan in bulk_scan mode (1–50)
enableMlAnalysis	boolean	false	No	Enable ML-powered analysis (anomaly detection, clustering, reports)
mlFeatures	object	{}	No	Toggle individual ML features (see ML Analysis section)

How to Find Event Slugs

1. Go to polymarket.com
2. Click on any event (e.g., "Presidential Election Winner 2028")
3. The URL will be: https://polymarket.com/event/presidential-election-winner-2028
4. The slug is: presidential-election-winner-2028

How to Find Wallet Addresses

1. Go to any market page on Polymarket
2. Click on a trader's profile
3. The URL contains the wallet address: https://polymarket.com/profile/0xd04d93...

How to Find Condition IDs

Each Polymarket market has a unique hex identifier called a condition ID (e.g., 0xdeb615a5...). You can find it in the Actor's output from any mode, or in Polymarket's API responses. Use condition IDs with alert_check mode for targeted monitoring.

---

ML Analysis (Optional)

Set enableMlAnalysis: true to activate machine learning-powered analysis on top of the standard metrics. All ML processing runs locally inside the Actor — no external AI APIs are called, no data leaves Apify infrastructure.

When ML is disabled (the default), the Actor output is identical to the standard version. When enabled, an mlInsights field is added to the output with the following components:

ML Features

Feature	Description	Toggle
Anomaly Detection	Isolation Forest algorithm detects unusual trading patterns across 5 dimensions (volume, trade count, whale ratio, price change, spread change)	mlFeatures.anomalyDetection
Whale Clustering	K-means groups whale traders by behavioral strategy instead of just position size	mlFeatures.whaleClustering
Statistical Enhancement	Trend detection via linear regression, confidence intervals for sentiment, cross-market correlation	mlFeatures.statisticalEnhancement
Report Generation	Template-based natural language summary with signals, risks, and actionable recommendation	mlFeatures.reportGeneration

ML Features Input Example

{
  "mode": "market_analysis",
  "eventSlugs": ["presidential-election-winner-2028"],
  "enableMlAnalysis": true,
  "mlFeatures": {
    "anomalyDetection": true,
    "whaleClustering": true,
    "statisticalEnhancement": true,
    "reportGeneration": true
  }
}

All four ML features default to true when enableMlAnalysis is enabled. You can selectively disable any feature to save processing time.

Whale Strategy Clusters

Instead of classifying whales only by position size, ML clustering groups them by behavioral patterns:

Cluster Label	Behavior Pattern
patient_value	High win rate + few trades — waits for high-conviction setups
active_momentum	High trade count + moderate win rate — active trading style
high_conviction	High ROI + positive P&L — concentrated bets that pay off
contrarian_risk	Low win rate — takes the other side of consensus

Anomaly Score

The anomaly detector scores each market from 0 to 1. A score above 0.7 flags the market as anomalous, with specific factors identified:

• volume_spike — Trading volume significantly above historical average
• whale_ratio_shift — Sudden change in whale vs retail participation
• price_change — Unusual price movement
• spread_change — Abnormal spread widening or tightening
• trade_count — Unusual number of individual trades

Recommendation Engine

The report generator produces one of four actionable recommendations:

Recommendation	Meaning
Opportunity	Multiple bullish signals + low risk — worth investigating
Monitor	Neutral or mixed signals — no clear edge
Caution	Bearish signals or elevated risk — proceed carefully
Avoid	High risk count (3+) — significant red flags detected

ML Output Example (mlInsights)

{
  "mlInsights": {
    "anomaly": {
      "anomalyScore": 0.85,
      "isAnomaly": true,
      "anomalyFactors": ["volume_spike", "whale_ratio_shift"]
    },
    "whaleClusters": [
      {
        "id": 0,
        "label": "patient_value",
        "memberCount": 5,
        "avgWinRate": 0.72,
        "avgRoi": 0.34,
        "avgTrades": 12,
        "members": ["0xabc...", "0xdef..."]
      },
      {
        "id": 1,
        "label": "active_momentum",
        "memberCount": 8,
        "avgWinRate": 0.55,
        "avgRoi": 0.18,
        "avgTrades": 89,
        "members": ["0x123...", "0x456..."]
      }
    ],
    "statistics": {
      "volumeTrend": {
        "direction": "increasing",
        "slope": 1250.5,
        "r2": 0.82
      },
      "sentimentConfidence": {
        "lower": 55,
        "upper": 89,
        "level": 0.95
      }
    },
    "report": {
      "summary": "Strong bullish momentum with whale accumulation. Anomalous volume spike detected — 3x above 7-day average. Patient value whales are dominant buyers.",
      "signals": [
        "Bullish: Whale sentiment +72",
        "Bullish: 80% buy-side volume"
      ],
      "risks": [
        "Anomaly: Volume spike (score 0.85)",
        "Concentration: HHI 0.35"
      ],
      "recommendation": "Opportunity"
    }
  }
}

ML Feature Availability by Mode

Feature	market_analysis	whale_profile	bulk_scan	alert_check
Anomaly Detection	Yes	—	—	Yes
Whale Clustering	Yes	—	—	—
Statistical Enhancement	Yes	—	Sector analysis	—
Report Generation	Yes	Yes	—	Yes (on alerts)

ML Design Principles

• Zero external API calls — All ML runs locally using npm packages (isolation-forest, ml-kmeans, simple-statistics, jstat)
• Graceful fallback — If any ML feature fails or lacks data, it returns null and the standard output remains intact
• Timer-aware — ML processing is skipped if the Actor is approaching its timeout budget (< 15 seconds remaining)
• Negligible overhead — Total ML processing adds < 10ms per market (CPU-only, no network calls)
• Off by default — Setting enableMlAnalysis: false (default) produces identical output to the non-ML version

---

Sample Output

Market Analysis Output

{
  "market": {
    "conditionId": "0x2d3c4fc5cde6dfb43448402b912e41bd...",
    "question": "Will Eric Trump win the 2028 US Presidential Election?",
    "slug": "will-eric-trump-win-the-2028-us-presidential-election",
    "category": "World Elections",
    "currentPrice": 0.0085,
    "volume24hr": 23286.52,
    "liquidity": 24781354.13,
    "openInterest": 0,
    "spread": 0.001
  },
  "smartMoneyMetrics": {
    "whaleSentiment": 0,
    "sentimentLabel": "neutral",
    "marketHealthScore": 54,
    "smartMoneyConsensus": {
      "yes": 0.38,
      "no": 0.62,
      "label": "moderate_bearish"
    },
    "concentrationHHI": 0.39
  },
  "whaleBreakdown": {
    "totalWhales": 40,
    "holders": [
      {
        "address": "0x0c0e270cf879583d6a0142fc817e05b768d0434e",
        "tier": "shark",
        "totalPositionValue": 345780.25,
        "winRate": 0.65,
        "roi": 0.34,
        "totalPnl": 87450,
        "resolvedTrades": 42,
        "topCategory": "politics"
      }
    ]
  },
  "tradeFlow": {
    "largeTrades": [
      {
        "wallet": "0xd04d93be590ded67b99f053d4b6d29d3f8483312",
        "side": "SELL",
        "size": 26600,
        "price": 0.992,
        "value": 26387.2,
        "timestamp": 1771982326,
        "walletTier": "unknown",
        "initiatedBy": "buyer"
      }
    ],
    "buyerInitiated": 26387.2,
    "sellerInitiated": 0,
    "totalVolume": 26387.2,
    "whaleVolume": 0,
    "whaleVolumeRatio": 0,
    "timeAnalysis": {
      "peakHour": 14,
      "quietHour": 3,
      "hourlyDistribution": {}
    }
  },
  "orderbook": {
    "bidDepth": 13361.95,
    "askDepth": 6309732.97,
    "imbalanceRatio": 1.16,
    "imbalanceLabel": "mild_bid",
    "bidLevels": 8,
    "askLevels": 54,
    "topBidLevels": [
      { "price": 0.008, "size": 364488.89 },
      { "price": 0.007, "size": 333295.37 }
    ],
    "topAskLevels": [
      { "price": 0.009, "size": 345016.06 },
      { "price": 0.01, "size": 595689.43 }
    ],
    "spreadAnalysis": {
      "bestBid": 0.008,
      "bestAsk": 0.009,
      "spread": 0.001,
      "spreadBps": 1176
    },
    "impact": {
      "impact_10k": 0.991,
      "impact_50k": 0.991,
      "impact_100k": 0.991
    }
  },
  "metadata": {
    "generatedAt": "2026-03-08T03:29:19.192Z",
    "mode": "market_analysis",
    "eventSlug": "presidential-election-winner-2028"
  }
}

Bulk Scan Output

{
  "question": "Will the Fed decrease interest rates by 50+ bps after the March 2026 meeting?",
  "slug": "will-the-fed-decrease-interest-rates-by-50-bps-after-the-march-2026-meeting",
  "conditionId": "0xdeb615a52cd114e5aa27d8344ae506a72bea81f6...",
  "category": "Economics",
  "currentPrice": 0.0015,
  "volume24hr": 11503957.55,
  "healthScore": 65,
  "topHolderCount": 2,
  "metadata": {
    "generatedAt": "2026-03-08T03:42:17.227Z",
    "mode": "bulk_scan"
  }
}

Whale Profile Output

{
  "wallet": "0xd04d93be590ded67b99f053d4b6d29d3f8483312",
  "summary": {
    "winRate": 0.65,
    "totalPnl": 12500.75,
    "roi": 0.34,
    "resolvedTrades": 42,
    "activePositionValue": 12450.75,
    "activePositionCount": 100
  },
  "activePositions": [
    {
      "conditionId": "0x098e2be3df8ab529940c567819f8ef007cf...",
      "size": 519292.67,
      "avgPrice": 0.4999,
      "initialValue": 259623.49,
      "currentValue": 778.94,
      "cashPnl": -258844.55,
      "percentPnl": -99.7,
      "curPrice": 0.0015,
      "title": "Will Australia win the 2026 FIFA World Cup?",
      "outcome": "Yes",
      "endDate": "2026-07-20"
    }
  ],
  "recentActivity": [],
  "metadata": {
    "generatedAt": "2026-03-08T03:38:18.355Z",
    "mode": "whale_profile"
  }
}

Alert Check Output

{
  "conditionId": "0x2d3c4fc5cde6dfb43448402b912e41bd...",
  "newLargeTrades": 1,
  "totalVolume": 26387.2,
  "trades": [
    {
      "proxyWallet": "0xd04d93be590ded67b99f053d4b6d29d3f8483312",
      "side": "SELL",
      "size": 26600,
      "price": 0.992,
      "timestamp": 1771982326,
      "title": "Will Eric Trump win the 2028 US Presidential Election?"
    }
  ],
  "metadata": {
    "generatedAt": "2026-03-08T03:31:20.560Z",
    "mode": "alert_check"
  }
}

---

Computed Metrics Explained

Smart Money Consensus

Measures the directional bias of top holders. Returns the ratio of whale capital on YES vs NO:

• yes: 0.8, no: 0.2 = 80% of whale capital is on YES

YES Ratio	Label
> 70%	strong_bullish
55–70%	moderate_bullish
45–55%	neutral
30–45%	moderate_bearish
< 30%	strong_bearish

Whale Sentiment Score (-100 to +100)

Net directional signal from whale traders. Positive = bullish, negative = bearish. Derived from trade history and position sizing.

Score Range	Label
75 and above	extreme_bullish
25 to 74	moderately_bullish
-24 to 24	neutral
-74 to -25	moderately_bearish
-75 and below	extreme_bearish

Market Health Score (0–100)

Composite score combining:

• 24-hour volume (25% weight)
• Liquidity depth (25% weight)
• Open interest (20% weight)
• Bid-ask spread — tighter is better (15% weight)
• Competitive score (15% weight)

Markets scoring below 40 are thin and susceptible to manipulation. Markets above 75 are healthy for larger positions.

Concentration HHI (0–1)

Herfindahl-Hirschman Index measuring how concentrated holdings are among top holders. Values above 0.25 indicate high concentration — fewer dominant holders controlling more of the market.

Orderbook Impact Simulation

Simulates the price impact of executing $10K, $50K, and $100K market orders against the live CLOB orderbook. Answers: "If I wanted to buy $50K YES right now, what would my average fill price be?"

Trade Flow Analysis

Breaks down recent large trades by:

• Buyer-initiated vs seller-initiated volume
• Whale vs non-whale volume ratio
• Hourly distribution (peak/quiet trading hours)

---

Whale Classification Tiers

Position sizes are classified into tiers based on total USD value:

Tier	Position Size	Description
Whale	$1,000,000+	Institutional-level capital
Shark	$100,000 – $999,999	Sophisticated individual traders
Dolphin	$10,000 – $99,999	Active retail with significant capital
Fish	$1,000 – $9,999	Standard retail positions
Retail	Below $1,000	Small positions

The whaleThreshold input parameter controls the minimum position size for inclusion in whale analysis (default: $50,000).

---

Pricing

This Actor uses Apify's Pay Per Event (PPE) billing model. You pay only for the analysis events you consume — no subscription, no minimum spend, no monthly fees.

Mode	Price per Run	What You Get
Market Analysis	$0.15 / market	Full analysis: metrics + whales + trade flow + orderbook
Whale Profile	$0.25 / wallet	Complete wallet: positions + P&L + win rate + activity
Bulk Scan	$1.50 / scan	Batch scan of top N markets with health scores
Alert Check	$0.05 / check	Lightweight large trade detection

Note: Market Analysis charges $0.15 per market within an event. An event with 20 markets costs 20 x $0.15 = $3.00.

ML analysis adds no extra cost — it runs locally on the same compute.

Estimated Monthly Costs

Usage Pattern	What You Do	Estimated Cost
Casual monitoring	1 bulk scan/day	~$45/month
Active trading	5 market analyses/day + hourly alerts	~$60/month
Research pipeline	Daily bulk scans + targeted analysis	~$120/month
Hourly alerts only	24 alert checks/day on 5 markets	~$7.50/month

Free Trial

Apify provides free credits for new accounts. You can test all four modes before committing to any spending.

---

API Integration

Run via Apify API (cURL)

curl -X POST \
  "https://api.apify.com/v2/acts/jy-labs~polymarket-whale-tracker/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "bulk_scan",
    "maxMarkets": 10
  }'

Run via Apify API (Python)

import requests

# Run the Actor and get results
response = requests.post(
    "https://api.apify.com/v2/acts/jy-labs~polymarket-whale-tracker/run-sync-get-dataset-items",
    params={"token": "YOUR_API_TOKEN"},
    json={
        "mode": "market_analysis",
        "eventSlugs": ["presidential-election-winner-2028"],
        "includeOrderbook": True,
        "includeTradeFlow": True
    }
)
markets = response.json()

for market in markets:
    print(f"{market['market']['question']}")
    print(f"  Health Score: {market['smartMoneyMetrics']['marketHealthScore']}")
    print(f"  Whale Sentiment: {market['smartMoneyMetrics']['sentimentLabel']}")
    print(f"  Consensus: {market['smartMoneyMetrics']['smartMoneyConsensus']['label']}")

Run via Apify API (JavaScript/Node.js)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('jy-labs/polymarket-whale-tracker').call({
    mode: 'whale_profile',
    walletAddresses: ['0xd04d93be590ded67b99f053d4b6d29d3f8483312']
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].summary);
// { winRate: 0.65, totalPnl: 12500, roi: 0.34, activePositionCount: 42 }

Run with ML Analysis (Python)

import requests

response = requests.post(
    "https://api.apify.com/v2/acts/jy-labs~polymarket-whale-tracker/run-sync-get-dataset-items",
    params={"token": "YOUR_API_TOKEN"},
    json={
        "mode": "market_analysis",
        "eventSlugs": ["presidential-election-winner-2028"],
        "enableMlAnalysis": True
    }
)
markets = response.json()

for market in markets:
    ml = market.get("mlInsights")
    if ml:
        report = ml.get("report", {})
        print(f"Summary: {report.get('summary')}")
        print(f"Recommendation: {report.get('recommendation')}")

        clusters = ml.get("whaleClusters", [])
        for c in clusters:
            print(f"  Cluster '{c['label']}': {c['memberCount']} whales, "
                  f"avg win rate {c['avgWinRate']:.0%}")

Schedule Automated Monitoring

To run alert_check every hour:

1. Open this Actor in Apify Console
2. Click Schedule in the top navigation
3. Set interval to Every 1 hour
4. Configure input:

   {
     "mode": "alert_check",
     "marketConditionIds": ["0xdeb615a52cd114e5..."],
     "tradeAlertThreshold": 5000
   }
   

5. Optionally add a webhook to receive results via Slack, email, or your API

Cost: ~$0.05/hour = $1.20/day for continuous whale monitoring.

---

MCP and AI Agent Integration

This Actor is fully compatible with the Apify MCP server, enabling direct integration with Claude Desktop, ChatGPT, and custom AI agent workflows.

Because it uses Pay Per Event pricing, each AI agent invocation produces a predictable, bounded cost — safe for automated pipelines.

Example: Claude Desktop via Apify MCP

Once the Apify MCP server is configured, ask Claude:

"Run a bulk scan of the top 10 Polymarket markets and tell me which ones have the highest health scores."

Claude will call this Actor, receive structured JSON, and interpret the results in natural language.

Example: ML-Powered Analysis via MCP

"Analyze the presidential election market on Polymarket with ML analysis enabled. Tell me if there are any anomalies and what the whale clusters look like."

Claude will run with enableMlAnalysis: true and interpret the mlInsights including anomaly scores, cluster strategies, and the natural language report.

---

Performance Guide

Mode	Scope	Recommended Memory	Recommended Timeout
Market Analysis	1 event (~10-130 markets)	256 MB	120s
Whale Profile	1–5 wallets	256 MB	60s
Bulk Scan	5–20 markets	256 MB	60s
Bulk Scan	20–50 markets	256 MB	120s
Alert Check	1–10 condition IDs	256 MB	30s

Tip: Each event can contain many sub-markets (e.g., "Presidential Election Winner 2028" has 128 candidate markets). Start with maxMarkets: 5 and increase as needed.

Enabling ML analysis adds negligible overhead (< 10ms per market). Memory requirements remain the same.

---

Technical Details

• Runtime: Node.js 20 on Apify Cloud
• Memory: 256 MB minimum, up to 4 GB for large scans
• Dependencies: No browser, no Puppeteer, no Playwright — pure API calls
• APIs used: Three Polymarket public REST APIs (no auth required)
• Rate limiting: Built-in sliding window rate limiter respecting each API's limits
• Error handling: Graceful per-market fallback — one failed market doesn't crash the entire run
• Timeout management: Automatic early exit when approaching Actor timeout budget
• ML libraries: isolation-forest, ml-kmeans, simple-statistics, jstat (loaded only when ML is enabled)
• Typical performance: Bulk scan of 20 markets completes in ~15 seconds

---

Frequently Asked Questions

Do I need a Polymarket account or API key? No. All three Polymarket APIs (Gamma, Data, CLOB) are public endpoints requiring no authentication. You only need an Apify account.

Does this Actor use a browser? No. It calls REST APIs directly — no browser automation, no DOM parsing. This makes it faster, more reliable, and less fragile than scraping.

How fresh is the data? Data reflects Polymarket's live state at runtime. CLOB orderbook data is real-time. Trade history is typically within minutes of live.

What if a Polymarket API is down? The Actor retries failed requests with exponential backoff for rate limits (429). If a specific market fails, it is skipped gracefully — other markets continue processing normally.

Why do some markets show 0 holders? The Data API's /holders endpoint may return limited data for newer or less active markets. The Actor handles this gracefully and still returns market metadata and orderbook data.

Does ML analysis call external AI APIs (OpenAI, Claude, etc.)? No. All ML processing uses local npm packages running inside the Actor container. No data is sent to any external AI service. This means no extra latency, no API keys needed, and no additional cost.

Why are all whale clusters labeled contrarian_risk? This happens when the Polymarket Data API returns empty closed position history for most wallets, resulting in 0% win rate across the board. The clustering algorithm correctly groups wallets with similar (low) win rates together. This is a data availability limitation, not a bug.

Can I use this for automated trading? This Actor provides information and analytics only. It does not execute trades. You can use the output to inform trading decisions or feed signals into your own trading system.

What is the difference between enableMlAnalysis: false and true? When false (default), the output contains standard computed metrics only. When true, an additional mlInsights field is added with anomaly detection, whale clustering, statistical analysis, and a natural language report. The standard fields remain identical in both cases.

---

Changelog

v1.1 (March 2026)

• Added optional ML analysis layer (enableMlAnalysis)
• Isolation Forest anomaly detection for unusual trading patterns
• K-means whale strategy clustering (4 behavioral labels)
• Statistical enhancement: trend detection, confidence intervals, correlation matrix
• Template-based NLG report with signals, risks, and recommendation
• All ML features are off by default and run locally (no external APIs)
• Granular ML feature toggles via mlFeatures object

v1.0 (March 2026)

• Initial release with 4 analysis modes
• Cross-API analytics (Gamma + Data + CLOB)
• Smart Money Consensus, Whale Sentiment, Market Health Score
• Orderbook depth analysis with impact simulation
• Trade flow analysis with hourly distribution
• Whale tier classification system
• Graceful error handling per market
• Built-in rate limiting for all 3 APIs

---

Legal Disclaimer

This Actor provides informational data only and does not constitute financial advice. The computed metrics, whale activity signals, ML-generated insights, and sentiment scores are derived from publicly available data and are intended for research and informational purposes only. Past patterns in whale trading behavior are not indicative of future market outcomes. Trading in prediction markets involves risk of financial loss. Users are solely responsible for any decisions made based on this data.