Polymarket Trades Dataset: How to Study Prediction Market Trade Flow

# Polymarket Trades Dataset: How to Study Prediction Market Trade Flow

Executed trades provide direct evidence of transactions that occurred in a prediction market. A structured Polymarket trades dataset can help researchers analyze volume, trade timing, execution prices, and market activity across historical contracts.

Trade data is useful for both descriptive research and realistic strategy testing.

What Is Included in Trade History?

A historical Polymarket trade record may include:

• Market identifier
• Token identifier
• Execution timestamp
• Trade price
• Trade quantity
• Transaction identifier
• Market side or direction
• Related event information

The exact fields depend on the source and collection method. Consistent identifiers and UTC timestamps make it easier to combine trade history with order book and market outcome data.

Studying Polymarket Trade Flow

Trade flow analysis examines how transactions occur over time. Researchers can group trades by market, minute, price range, size, or direction estimate.

Useful measurements include:

• Total market volume
• Number of trades
• Average trade size
• Median trade size
• Volume by time interval
• Trade concentration
• Price movement after large trades
• Activity near market resolution

These metrics can reveal whether trading is evenly distributed or concentrated during specific periods.

Trade Data and Order Books

Trade records are more informative when combined with historical order book data. The order book shows available liquidity, while trades show completed interactions with that liquidity.

Researchers can compare:

• Execution price and midpoint
• Trade size and displayed depth
• Spread before and after a trade
• Trade activity during liquidity changes
• Price movement after quote removal
• Volume during periods of market imbalance

This combined analysis can support Polymarket market microstructure research.

Using Trades in Backtesting

A trading strategy should not assume that every historical signal led to a completed order. Executed trade data can help create more realistic assumptions about market activity and liquidity.

A backtest may use trades to:

• Confirm market activity
• Estimate execution probability
• Model trade timing
• Analyze slippage
• Measure volume participation
• Compare simulated fills with actual transactions

However, trade history alone may not show all available liquidity. For execution simulation, it is better to combine trades with order book snapshots or level changes.

Trade Flow Features for Machine Learning

Developers can derive features from Polymarket trade history, including:

• Rolling trade count
• Rolling volume
• Average trade size
• Trade-size volatility
• Time since last trade
• Recent price movement
• Volume acceleration
• Activity relative to market age
• Activity relative to time remaining

Features must be calculated using only records available before the prediction or trading timestamp. Future trades and final outcomes should remain outside the feature set.

Finding Research-Ready Polymarket Data

polytestdata.xyz offers historical Polymarket datasets prepared for trade analysis, order book research, backtesting, and trading bot development. The products focus on selected short-duration crypto markets and provide structured downloadable files.

Historical trade data can improve research workflows, but it cannot guarantee a profitable strategy. All datasets are research products and should not be considered financial advice.