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:

1. Find market ID via search:

curl "https://api.manifold.markets/v0/search-markets?term=your+search+term"

2. Fetch all bets with pagination:

curl "https://api.manifold.markets/v0/bets?contractId=MARKET_ID&limit=1000"
# Use &before=LAST_BET_ID for pagination

3. 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:

1. Mana is play money with no cash value
2. Large positions may represent accumulated winnings, not money invested
3. New users get Ṁ1,000 free; active traders earn daily bonuses
4. Someone with Ṁ40k may have spent $0-400 actual dollars