Building a Machine Learning Dataset from Polymarket Historical Markets

# Building a Machine Learning Dataset from Polymarket Historical Markets

Historical Polymarket data can be used to create datasets for machine learning experiments, statistical modeling, and prediction market research. The quality of the final model depends heavily on data preparation, timestamp alignment, feature design, and leakage prevention.

A structured Polymarket dataset provides the raw records needed to create repeatable experiments.

Defining the Prediction Target

The first step is defining the target variable. Examples include:

• Market outcome at resolution
• Price movement over the next interval
• Probability change over a defined period
• Spread widening
• Liquidity decline
• Trade activity acceleration

The target must be linked to a clear timestamp. For example, a model may use information available at minute two to estimate a market condition at minute four.

Useful Polymarket Features

Historical market data can produce features such as:

• Current token price
• Recent price return
• Rolling volatility
• Bid-ask spread
• Midpoint price
• Order book imbalance
• Available quantity near the midpoint
• Recent trade count
• Recent trading volume
• Average trade size
• Time remaining until resolution
• Market age

For crypto prediction markets, additional external features may include underlying asset price changes, volatility, and reference-market conditions. These external records must be aligned carefully with Polymarket timestamps.

Preventing Training Data Leakage

Leakage occurs when a model receives future information. In prediction market research, common examples include using the final outcome as a feature, calculating a rolling value with future records, or randomly splitting observations from the same market across training and testing data.

Better practices include:

• Use chronological splits.
• Group observations by market.
• Calculate rolling values backward from each timestamp.
• Separate features from future labels.
• Test on markets not used during training.
• Preserve the original event order.

These practices create a more realistic estimate of model performance.

Handling Imbalanced Outcomes

If one outcome appears more frequently than another, accuracy may become misleading. Researchers should review class balance and use metrics that reflect the actual objective.

Useful evaluation metrics may include:

• Precision
• Recall
• F1 score
• Log loss
• Brier score
• Calibration error
• Confusion matrix
• Area under the precision-recall curve

For probability models, calibration can be more informative than simple classification accuracy.

Why Order Book Data Helps

Order book data adds information about market conditions that price history alone may miss. A market with the same midpoint price can have very different depth, spread, and trade activity.

Order book features can help measure:

• Available liquidity
• Market pressure
• Quote stability
• Execution difficulty
• Imbalance between buying and selling interest

Snapshots and order book deltas can be used to create both low-frequency and tick-level features.

Evaluating Generalization

A model that performs well on one group of markets may not generalize to another. Researchers should compare results across:

• BTC, ETH, SOL, and XRP markets
• Different volatility regimes
• Different market times
• Different liquidity levels
• Early and late market periods
• Multiple historical date ranges

polytestdata.xyz provides historical Polymarket crypto data that can support feature engineering, machine learning experiments, and outcome analysis.

Machine learning results are research findings, not financial guarantees. Historical performance cannot predict future market behavior with certainty.