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About polymarket-whale-watcher

AI-powered whale trade surveillance for Polymarket β€” real-time monitoring of 700+ markets, 14 autonomous research tools, 7-step deep analysis with information asymmetry scoring.

Platforms

Web Self-hosted

Languages

Python

Links

πŸ‹ Polymarket Whale Watcher

AI-Powered Whale Trade Intelligence for Polymarket Prediction Markets

Python 3.10+ License: MIT Polymarket Gemini


Real-time monitoring of 700+ markets  Β·  14 autonomous research tools  Β·  Multi-step deep analysis  Β·  Signal accuracy tracking


Quick Start   |   How It Works   |   Sample Report   |   Configuration   |   Dashboard


pipeline


What It Does

Whale Watcher continuously monitors 700+ active Polymarket markets across three volume tiers, detects large trades with anomalous patterns, and deploys an LLM agent with 14 autonomous research tools to conduct multi-step deep investigations. Each whale trade undergoes a structured 7-step analysis pipeline β€” from trader profiling and cross-market position mapping to information gap assessment β€” producing an Information Asymmetry Score that quantifies the likelihood of non-public information advantage.


Live Demo

Terminal Output β€” Real-time whale detection and analysis


$ python -m src.main run

============================================================
WHALE WATCHER STARTED
============================================================
Monitoring: 765 markets
Interval: 10 seconds
Min Trade Size: $10,000 USD
Price Range: 0.1 - 0.9
============================================================

Tiered monitoring: Tier1=8 (>500K), Tier2=198 (>10K), Tier3=559 (>1K)

[23:41:12] WHALE TRADE DETECTED!
           Amount: $9,600.00 USDC
           Side: BUY Yes
           Price: 0.7142
           Market: US x Iran diplomatic meeting by June 30, 2026?

           Generating analysis report...
           Round 1: LLM requested 3 tool call(s)
           β†’ search_web("US Iran diplomatic meeting June 2026")
           β†’ search_twitter("US Iran meeting diplomacy")
           β†’ get_wallet_transfers("0xceza...rn132")
           Round 2: LLM requested 2 tool call(s)
           β†’ search_web("Islamabad Iran talks Witkoff April 2026")
           β†’ search_twitter("POLYMARKET Iran meeting odds fading")
           Round 3: LLM requested 1 tool call(s)
           β†’ search_web("Iran FM Araghchi 3 phase deal proposal")

           Analysis complete after 3 round(s)
           Information Asymmetry Score: 0.32 (LOW)
           Trader Credibility: MEDIUM (#1733, PnL: $84K)
           Verdict: Thesis continuation / loss recovery β€” HOLD/PASS
           Report saved: reports/20260504/...


Sample Report β€” 7-Step Deep Analysis (click to expand)


Each whale trade generates a comprehensive markdown report with structured multi-step analysis:

Full example: docs/examples/sample_report.md

Report Structure

======================================================================
# Whale Trade Analysis Report
======================================================================

β”Œβ”€ Trade Summary ─────────────────────────────────────────────────────┐
β”‚  Market, trade size, direction, price, odds, time, trader rank     β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 2: Trade Signal Analysis ─────────────────────────────────────┐
β”‚  Trader profile: rank, PnL, avg size, large trade ratio            β”‚
β”‚  Domain expertise detection, trade timing analysis                  β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 3: Event-Related Position Analysis ───────────────────────────┐
β”‚  Cross-market positions, roll-forward detection                     β”‚
β”‚  Loss recovery patterns, hedge identification                       β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 4: Market Long/Short Analysis ────────────────────────────────┐
β”‚  Top 5 bulls & bears with rankings and PnL                          β”‚
β”‚  Smart money consensus assessment                                   β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 5: Information Gap Analysis ──────────────────────────────────┐
β”‚  Public information audit (web, Twitter, Telegram)                  β”‚
β”‚  Market pricing efficiency check                                    β”‚
β”‚  Non-public information evidence search                             β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 6: Historical Pattern ────────────────────────────────────────┐
β”‚  Trader's past bets on related events                               β”‚
β”‚  Strategy pattern recognition (laddering, hedging, etc.)            β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”Œβ”€ Step 7: Information Asymmetry Assessment ──────────────────────────┐
β”‚  Score (0–1), trader credibility, evidence, reasoning               β”‚
β”‚  Multi-factor summary table with signal strength                    β”‚
β”‚  Recommended action: BUY / HOLD / PASS                              β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Example Summary Table

Factor Assessment Signal
Trader Rank/PnL Rank #1733, $84K PnL Moderate
Trade Size vs. Normal ~$9.6K vs. avg $10K Routine β€” neutral
Related Position Heavy loser on May 15 market (-$6.5K) Suppresses signal
Domain Expertise Iran geopolitics specialist Supportive
Public Info Coverage Extensive public news Reduces asymmetry
Smart Money Bulls Rank #278 also long Modest support
Overall Thesis continuation, not insider signal Low-Medium


Daily Briefing β€” Automated intelligence summary


Daily briefings are generated at 10:00 AM local time and emailed automatically.

Full example: docs/examples/sample_briefing.md

Includes:

  • High-confidence signals (IAS >= 60%) with full analysis summaries
  • Fallback: top 5 signals by score if none reach the threshold
  • Abnormal price volatility alerts
  • Historical signal performance β€” win rate and ROI by confidence tier



Quick Start

One-Click Setup

git clone https://github.com/chaoleiyv/polymarket-whale-watcher.git
cd polymarket-whale-watcher
chmod +x setup.sh && ./setup.sh

The setup script will:

  1. Check Python 3.10+ is installed
  2. Create a virtual environment
  3. Install all dependencies
  4. Create .env from template

Then add your API key and start:

# Add your Gemini API key (the only required key)
echo "GEMINI_API_KEY=your_key_here" >> .env

# Activate the environment and run
source .venv/bin/activate
python -m src.main run

Get a free Gemini API key: https://aistudio.google.com/apikey

Docker

docker build -t whale-watcher .
docker run --env-file .env -v ./data:/app/data -v ./reports:/app/reports whale-watcher

How It Works

flowchart LR
    A[Polymarket API] --> B[Market Fetcher]
    B --> C[700+ Tiered Markets]
    C --> D[Trade Monitor]
    D --> E{Whale\nTrade?}
    E -->|No| D
    E -->|Yes| F[Anomaly Detector]
    F --> G{Score >= 0.65?}
    G -->|No| D
    G -->|Yes| H[LLM Analyzer]
    H --> I[14 Research Tools]
    I --> J[Signal + Report]
    J --> K[Resolution Tracker]
    K --> L[Dashboard + Email]

Pipeline

Stage What Happens
1. Market Selection Fetches all active markets from Polymarket Gamma API, classifies into 3 tiers by 24h volume, adds token launch markets. Refreshes every 15 minutes.
2. Trade Monitoring Parallel async tasks per market (700+), polls official Polymarket data-api for new taker BUY trades, deduplicates by transaction hash. Connection pool: 50 connections, 120s timeout.
3. Whale Pre-filter Price range 0.10–0.90, $5K hard floor, dynamic threshold scaled by volume ($5K–$100K), conviction check (must pay above mid), resolution window 6h–90d.
4. Anomaly Scoring 5-factor model (max 1.0): base confidence (0.50) + premium ratio (0.20) + signal cleanliness (0.10) + depth ratio (0.10) + cluster tier (0.10). Threshold: >= 0.65.
5. LLM Investigation Builds rich context (trade + trader profile + event positions + market top holders + historical signals). LLM autonomously selects tools for up to 3 rounds. Produces structured 7-step analysis with information asymmetry score (0–1).
6. Signal Tracking Resolution tracker checks every 30 min, validates signal correctness, computes theoretical ROI. Daily briefings at 10:00 AM, emailed to recipients.

Features

Feature Description
Tiered Market Monitoring 700+ markets across 3 tiers: Tier1 (>$500K, 15s), Tier2 (>$10K, 60s), Tier3 (>$1K, 300s)
5-Factor Anomaly Detection Premium ratio, signal cleanliness, depth ratio, cluster signals, base confidence
7-Step Deep Analysis Trade signal β†’ Event positions β†’ Long/short mapping β†’ Info gap β†’ Historical pattern β†’ Asymmetry score
14 Autonomous Research Tools Web, Twitter, Telegram, crypto, DeFi, stocks, on-chain, legislation
Cross-Market Position Analysis Detects roll-forwards, hedges, and loss recovery patterns across related markets
Signal Accuracy Tracking Auto resolution checking every 30 min, win rate stats by confidence tier
Daily Briefings 10:00 AM automated summary with high-confidence signals, emailed to recipients
Real-time Email Alerts Instant notifications for high information-asymmetry signals (>= 60%)
Web Dashboard FastAPI-based signal performance dashboard with ROI breakdowns

14 LLM Research Tools

The LLM agent autonomously selects and chains these tools during its multi-round investigation:

Category Tools Use Case
Social & Sentiment search_twitter Β· search_telegram Β· search_web Public sentiment, insider chatter, news coverage
Crypto & DeFi get_crypto_price Β· get_crypto_market_overview Β· get_protocol_tvl Β· get_token_unlocks Β· get_protocol_revenue Token prices, TVL, unlocks, protocol health
Financial Data get_stock_price Β· get_stock_news Β· get_economic_data Equities, ETFs, macro indicators
On-Chain get_wallet_transfers Β· get_contract_info Wallet activity, contract deployments
Legislation get_bill_status Β· get_recent_legislation US bills, regulatory actions

Sample Report

Every whale trade produces a structured multi-step report. Here's a condensed view:

======================================================================
# Whale Trade Analysis Report
======================================================================

Trade Summary
  Market: US x Iran diplomatic meeting by June 30, 2026?
  Size: $9,600 USDC  |  Direction: BUY Yes (71.4%)  |  Trader: #1733

Step 2: Trade Signal Analysis
  β†’ Mid-tier trader, $84K PnL, Iran geopolitics specialist
  β†’ Trade size ($9.6K) matches avg ($10K) β€” routine, not exceptional

Step 3: Event-Related Position Analysis            ← KEY FINDING
  β†’ Losing -$6,508 on earlier "May 15 meeting" market (15.5% odds)
  β†’ This trade is a thesis roll-forward, not a fresh insider bet

Step 4: Market Long/Short Analysis
  β†’ Biggest Yes holder is a chronic loser (PnL: -$5.5M) β€” red flag
  β†’ One elite trader (Rank #278) also long β€” modest support

Step 5: Information Gap Analysis
  β†’ All supporting info widely reported in mainstream media
  β†’ Market at 69.5% β€” already fairly priced

Step 6: Historical Pattern
  β†’ "Timeline ladder" strategy across multiple Iran-related deadlines

Step 7: Information Asymmetry Assessment
  β†’ Score: 0.32 (LOW)  |  Credibility: MEDIUM
  β†’ Verdict: Thesis continuation / loss recovery β€” HOLD/PASS

======================================================================

Full report: docs/examples/sample_report.md


Configuration

Copy .env.example to .env and configure:

Required

Variable Description Get It
GEMINI_API_KEY LLM API key for analysis Google AI Studio

Optional (enhances analysis quality)

Data Source API Keys
Variable Description Get It
TAVILY_API_KEY Web search (primary) tavily.com
SERPER_API_KEY Web search (fallback) serper.dev
TWITTER_API_KEY Twitter sentiment search twitterapi.io
POLYGON_API_KEY Stock/ETF/forex data polygon.io
FRED_API_KEY Economic indicators FRED
ETHERSCAN_API_KEY On-chain wallet analysis (Polygon V2) etherscan.io
CONGRESS_API_KEY US legislation data congress.gov
TELEGRAM_API_ID / TELEGRAM_API_HASH Telegram channel monitoring my.telegram.org
LLM Settings
Variable Default Description
LLM_MODEL gemini-3-flash-preview Model name (any OpenAI-compatible)
LLM_BASE_URL Google AI endpoint OpenAI-compatible API base URL
LLM_TEMPERATURE 0 LLM temperature
Whale Detection Tuning
Variable Default Description
MIN_TRADE_SIZE_USD 10000 Minimum trade size to consider
MIN_PRICE / MAX_PRICE 0.10 / 0.90 Price range filter
FETCH_INTERVAL_SECONDS 10 Default polling interval
Tiered Market Monitoring
Variable Default Description
FULL_MARKET_SCAN true Enable tiered monitoring (all active markets)
TIER1_VOLUME_MIN 500000 Tier 1 volume threshold
TIER2_VOLUME_MIN 10000 Tier 2 volume threshold
TIER3_VOLUME_MIN 1000 Tier 3 volume threshold
TIER1_POLL_INTERVAL 15 Tier 1 polling interval (seconds)
TIER2_POLL_INTERVAL 60 Tier 2 polling interval (seconds)
TIER3_POLL_INTERVAL 300 Tier 3 polling interval (seconds)
Email Alerts
Variable Default Description
EMAIL_ENABLED false Enable email notifications
EMAIL_SENDER β€” Sender email address
EMAIL_PASSWORD β€” Sender email password (app password)
EMAIL_RECIPIENT β€” Comma-separated recipient emails

Commands

# Core
python -m src.main run [--debug]              # Start monitoring
python -m src.main check-markets --limit 20   # View trending markets

# Analysis
python -m src.main test-analyze <market_id>   # Test LLM on a specific market

# Reports
python -m src.main briefing --today           # Generate today's briefing
python -m src.main briefing --date 2026-04-17 # Briefing for a specific date

# Dashboard
python -m src.main dashboard --port 8000      # Start web dashboard

# Maintenance
python -m src.main migrate                    # Migrate legacy JSON to SQLite

Dashboard

python -m src.main dashboard
# Open http://localhost:8000

The dashboard shows:

  • Overall signal statistics (total signals, win rate, avg ROI)
  • Performance breakdown by confidence tier
  • Top best/worst signals by theoretical ROI
  • Paginated signal history

Architecture

                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚     Polymarket Gamma API      β”‚
                    β”‚   (all active markets)        β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚        Market Fetcher         β”‚
                    β”‚  Tier1: >$500K  (15s poll)    β”‚
                    β”‚  Tier2: >$10K   (60s poll)    β”‚
                    β”‚  Tier3: >$1K    (300s poll)   β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              β”‚       Trade Monitor (async, 700+)       β”‚
              β”‚   Official Polymarket data-api           β”‚
              β”‚   Per-market parallel tasks              β”‚
              β”‚   Pool: 50 connections, 120s timeout     β”‚
              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              β”‚          Pre-filter + Scoring            β”‚
              β”‚  $5K+ size, 0.10-0.90 price, conviction β”‚
              β”‚  5-factor anomaly score >= 0.65          β”‚
              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                   β”‚
              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              β”‚     LLM Analyzer β€” 7-Step Pipeline      β”‚
              β”‚   14 tools Β· up to 3 rounds             β”‚
              β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
              β”‚ Twitter  β”‚  Web   β”‚ DeFi   β”‚  On-Chain  β”‚
              β”‚ Telegram β”‚ Search β”‚ Crypto β”‚ Legislationβ”‚
              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”¬β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                             β”‚
              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              β”‚     Signal Storage (SQLite)             β”‚
              β”‚  β†’ Resolution Tracker (every 30min)    β”‚
              β”‚  β†’ Daily Briefing (10:00 AM + email)   β”‚
              β”‚  β†’ Email Alerts (IAS >= 60%)           β”‚
              β”‚  β†’ Dashboard (FastAPI)                 β”‚
              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Project Structure

src/
β”œβ”€β”€ config/settings.py              # Environment configuration (Pydantic)
β”œβ”€β”€ models/                         # Data models
β”‚   β”œβ”€β”€ market.py                   # Market, TrendingMarket
β”‚   β”œβ”€β”€ trade.py                    # TradeActivity, WhaleTrade, TraderRanking
β”‚   β”œβ”€β”€ decision.py                 # TradeRecommendation, LLMDecision
β”‚   └── anomaly_signal.py           # AnomalySignal (stored signal)
β”œβ”€β”€ services/                       # Business logic
β”‚   β”œβ”€β”€ market_fetcher.py           # Polymarket Gamma API (tiered market selection)
β”‚   β”œβ”€β”€ trade_monitor.py            # Per-market parallel monitoring (official API)
β”‚   β”œβ”€β”€ anomaly_detector.py         # 5-factor anomaly scoring
β”‚   β”œβ”€β”€ llm_analyzer.py             # LLM with tool-use (14 tools, 3 rounds)
β”‚   β”œβ”€β”€ tools.py                    # Tool registry
β”‚   β”œβ”€β”€ resolution_tracker.py       # Market resolution checking
β”‚   β”œβ”€β”€ stats_engine.py             # Performance statistics
β”‚   β”œβ”€β”€ daily_briefing.py           # Daily summary generation + email
β”‚   β”œβ”€β”€ twitter_search.py           # Twitter API search
β”‚   β”œβ”€β”€ telegram_search.py          # Telegram channel monitoring
β”‚   β”œβ”€β”€ web_search.py               # Tavily/Serper/DuckDuckGo search
β”‚   β”œβ”€β”€ coingecko.py                # Crypto prices and market data
β”‚   β”œβ”€β”€ defillama.py                # DeFi TVL, revenue, token unlocks
β”‚   β”œβ”€β”€ fred.py                     # FRED macroeconomic data
β”‚   β”œβ”€β”€ polygon.py                  # Stock/ETF prices and news
β”‚   β”œβ”€β”€ etherscan.py                # On-chain data (Polygon, Etherscan V2)
β”‚   └── congress.py                 # US legislation data
β”œβ”€β”€ prompts/                        # LLM system prompts
β”‚   β”œβ”€β”€ whale_analyzer.py           # Whale trade analysis prompt
β”‚   └── volatility_analyzer.py      # Price volatility analysis prompt
β”œβ”€β”€ db/database.py                  # SQLite signal storage
└── main.py                         # CLI entry point (Typer)

Disclaimer

This system is for research and educational purposes only. Prediction market trading involves significant risk. The information asymmetry scores and analyses are AI-generated estimates β€” not financial advice. Always conduct your own research and verify independently before making any trading decisions.


MIT License  Β·  Built with Polymarket API + Gemini