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