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Access complete prediction market data via daily bulk file exports in your preferred format.

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Getting Started

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Access Files

Log into app.probalytics.io and navigate to the Files section.

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Select Data

Use the interface to choose:
  • Platform: Polymarket or Kalshi
  • Entity type: Markets or Trades
  • Frequency: Available exports depend on entity:
    • Markets: Daily, Weekly, Monthly
    • Trades: Daily, Weekly
  • File: Browse available exports in the file tree
Select download files
Once a file is selected, the Download button appears in the top right. Click to download.

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File Formats

All files are automatically gzipped (.gz). Choose your format based on use case: CSV
  • Best for: Excel, data analysis, simple processing
  • Field headers included
  • Standard CSV formatting
Parquet
  • Best for: Analytics queries, data science, large datasets
  • Columnar format, highly compressed
  • Native support: Python (pandas, polars), R, Go, Java
  • Best performance for analytical queries
JSONL
  • Best for: Integration with APIs, JavaScript/Node.js
  • One JSON object per line
  • Standard JSON formatting

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File Naming

Files follow this pattern: 
{entity}_{platform}_{date_or_range}_{random_id}.{format}.gz
Examples: 
trades_polymarket_2024-01-15_a7k9m2b1.csv.gz
markets_kalshi_2024-01-10_to_2024-01-15_x3l8n9q2.parquet.gz
markets_polymarket_2024-01_m7q1k3n5.jsonl.gz
Files are created on the following schedule:
  • Daily: 00:15 UTC (all entities)
  • Weekly: Mondays at 00:15 UTC (Markets and Trades)
  • Monthly: First day of month at 00:15 UTC (Markets only)

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Parsing Examples

Choose examples based on your use case and language. All formats can be decompressed on-the-fly.

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Python

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Load and Explore with Pandas

Best for quick analysis and exploration. 
import pandas as pd

df = pd.read_parquet('trades_polymarket_2024-01-15_a7k9m2b1.parquet.gz')

print(df.head())
print(df.dtypes)
print(df.describe())

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Load and Explore with Polars

Faster for large files, better performance. 
import polars as pl

df = pl.read_parquet('trades_polymarket_2024-01-15_a7k9m2b1.parquet.gz')

print(df.head())
print(df.schema)
print(df.describe())

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Query and Filter with Polars

Efficient filtering with lazy evaluation. 
import polars as pl

df = pl.read_parquet('trades_polymarket_2024-01-15_a7k9m2b1.parquet.gz')

# High-value trades
high_value = df.filter(pl.col('amount') > 1000)
print(high_value)

# Group by platform and sum volume
by_platform = df.groupby('platform').agg(pl.col('volume').sum())
print(by_platform)

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CSV or JSONL - Standard Library

Lightweight parsing without dependencies. 
import gzip
import json

with gzip.open('trades_polymarket_2024-01-15_a7k9m2b1.jsonl.gz', 'rt') as f:
    for line in f:
        trade = json.loads(line)
        if float(trade['price']) > 0.8:
            print(trade['market_id'], trade['price'])

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JavaScript / Node.js

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Load and Explore

import fs from 'fs';
import { createGunzip } from 'zlib';
import { parse } from 'csv-parse/sync';

const file = fs.readFileSync('trades_polymarket_2024-01-15_a7k9m2b1.csv.gz');
const decompressed = require('zlib').gunzipSync(file);
const data = parse(decompressed, { columns: true });

console.log(data.slice(0, 5));
console.log(`Total rows: ${data.length}`);

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Stream JSONL

import fs from 'fs';
import { createGunzip } from 'zlib';
import { createInterface } from 'readline';

const rl = createInterface({
  input: fs.createReadStream('trades_polymarket_2024-01-15_a7k9m2b1.jsonl.gz')
    .pipe(createGunzip()),
  crlfDelay: Infinity
});

rl.on('line', (line) => {
  const trade = JSON.parse(line);
  if (parseFloat(trade.price) > 0.8) {
    console.log(trade.market_id, trade.price);
  }
});

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Filter and Aggregate

import fs from 'fs';
import { createGunzip } from 'zlib';
import { parse } from 'csv-parse/sync';

const file = fs.readFileSync('trades_polymarket_2024-01-15_a7k9m2b1.csv.gz');
const decompressed = require('zlib').gunzipSync(file);
const data = parse(decompressed, { columns: true });

// Filter high-price trades
const highPrice = data.filter(row => parseFloat(row.price) > 0.8);

// Sum volume by platform
const byPlatform = data.reduce((acc, row) => {
  const platform = row.platform;
  acc[platform] = (acc[platform] || 0) + parseFloat(row.volume);
  return acc;
}, {});

console.log(byPlatform);

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Common Workflows

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Weekly Export Analysis

Download the weekly trade export for comprehensive weekly analysis: 
import pandas as pd

# Weekly export (created every Monday)
trades = pd.read_parquet('trades_polymarket_2024-01-14_w2k7m1n3.parquet.gz')

print(f"Total trades: {len(trades)}")
print(trades.groupby('platform')['volume'].sum())

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Monthly Market Snapshot

Get a complete monthly snapshot of markets: 
import pandas as pd

# Monthly markets export (created on 1st of month)
markets = pd.read_parquet('markets_kalshi_2024-01_m7q1k3n5.parquet.gz')

print(f"Total markets: {len(markets)}")
print(markets.groupby('category')['status'].value_counts())

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Stream JSONL Records

Process JSONL records line-by-line: 
import gzip
import json

with gzip.open('trades_polymarket_2024-01-15_a7k9m2b1.jsonl.gz', 'rt') as f:
    for line in f:
        trade = json.loads(line)
        # Process each trade immediately
        if float(trade['price']) > 0.8:
            print(f"High price trade: {trade['market_id']} at {trade['price']}")

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Data Schema

Files contain the same data as REST API responses. Refer to the REST API Reference for:
  • Complete field definitions
  • Data types and formats
  • Example values