diegosouzapw Manifold Markets Analysis
Analyze prediction market data from Manifold Markets to create interactive visualizations and trader analytics.
Overview
Manifold Markets is a play-money prediction market platform. Key concepts:
- Mana (Ṁ) - Play-money currency (not convertible to cash, ~Ṁ100 = $1 purchase price)
- Markets - Questions with multiple answer buckets (e.g., "$5-10B", ">$25B")
- Trading - Users buy YES/NO shares on answers; prices reflect probability
Data Sources
Manifold API (Preferred)
Fetch data directly from the Manifold Markets API:
- Find market ID via search:
curl "https://api.manifold.markets/v0/search-markets?term=your+search+term"
- Fetch all bets with pagination:
curl "https://api.manifold.markets/v0/bets?contractId=MARKET_ID&limit=1000"
# Use &before=LAST_BET_ID for pagination
- Resolve usernames for top traders:
curl "https://api.manifold.markets/v0/user/by-id/USER_ID"
Rate Limiting: Be conservative - 1 second between paginated requests, longer for user lookups. Skip bulk user lookups if possible.
Use scripts/fetch_market_data.py for automated fetching:
python3 scripts/fetch_market_data.py --market-id MARKET_ID --output all > market_data.json
HTML Export
Users may upload saved HTML from manifold.markets pages. Extract data from:
- Market title and metadata in page header
- Trade history in comments/activity sections (look for patterns like "bought Ṁ50 of YES")
- Current probabilities displayed for each answer
Trade History Text
Users may paste trade history directly. Common format:
Username,action,amount,answer,outcome,time_ago
JoshYou,bought,350,>$25B,YES,1y
Bayesian,sold,100,$5-10B,NO,3mo
Time formats: 23d (days), 1mo/3mo (months), 1y (year ago)
Analysis Workflow
1. Parse Trade Data
Use scripts/parse_trades.py to extract trades from text:
python3 scripts/parse_trades.py < trades.txt > trades.json
2. Aggregate by Trader
For each trader compute:
- Total volume (sum of all trade amounts)
- Trade count
- Buy/sell ratio
- YES vs NO volume breakdown
- Top answer buckets traded
3. Aggregate by Time
Convert relative timestamps to approximate dates:
- Reference: current date or market close date
- Map "1y" → ~12 months ago, "3mo" → ~3 months ago, etc.
- Group by month for time series
4. Create Visualization
Build an HTML visualization with Chart.js (preferred for reliability):
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js"></script>
Include:
- Cumulative stacked area chart by answer over time
- Trader leaderboard table with volume, trades, YES/NO breakdown
- Answer breakdown legend with colors
- Stats cards showing probability, total volume, trades, unique traders
See references/visualization_template.md for React/Recharts approach (less reliable CDN loading).
Example output: iran_market_viz_chartjs.html - full standalone visualization
Color Scheme for Answers
Binary Markets (YES/NO)
const colors = {
YES: '#10b981', // Green - teal
NO: '#ef4444' // Red
};
Multi-Answer Markets
Use consistent colors across visualizations:
const colors = {
"<$5B": "#99DDFF",
"$5-10B": "#FFDD99",
"$10.1-12.5B": "#FFAABB",
"$12.6-15B": "#77F299",
"$15.1-17.5B": "#CD46EA",
"$17.6-20B": "#F23542",
"$20.1-25B": "#FF8C00",
">$25B": "#44BB99"
};
Adapt color keys to match actual answer labels in the market.
Key Metrics to Surface
Market Level
- Total volume traded
- Number of unique traders
- Peak trading month
- Current leading answer and probability
Trader Level
- Rank by total volume ("whales")
- Rank by trade count ("most active")
- YES vs NO ratio (bullish/bearish tendency)
- Top 2-3 answers traded per user
Insights to Highlight
- Biggest whale - Highest total volume
- Most active - Highest trade count
- Top bull - Highest % YES volume
- Top bear - Highest % NO volume
Context Notes
When presenting analysis, note:
- Mana is play money with no cash value
- Large positions may represent accumulated winnings, not money invested
- New users get Ṁ1,000 free; active traders earn daily bonuses
- Someone with Ṁ40k may have spent $0-400 actual dollars