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manja316/crypto-prediction-market-signals

Crypto + Prediction Market Cross-Signal Dataset BTC, ETH, SOL prices + funding rates + open interest + gold + Polymarket crypto probabilities — synced at 15-minute intervals. The only dataset that combines crypto market microstructure with prediction market sentiment in one place. What's Inside Table Rows Description candles 8,800+ 15-min OHLCV for BTC, ETH, SOL (Binance Futures) funding_rates 460+ 8-hourly funding rates + mark prices open_interest… See the full description on the dataset page: https://huggingface.co/datasets/manja316/crypto-prediction-market-signals.

sourceHugging Facecc-by-4.0updated 5mo agoView on Hugging Face
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Dataset Card

Crypto + Prediction Market Cross-Signal Dataset

BTC, ETH, SOL prices + funding rates + open interest + gold + Polymarket crypto probabilities — synced at 15-minute intervals.

The only dataset that combines crypto market microstructure with prediction market sentiment in one place.

What's Inside

TableRowsDescription
candles8,800+15-min OHLCV for BTC, ETH, SOL (Binance Futures)
funding_rates460+8-hourly funding rates + mark prices
open_interest190+Open interest in contracts + USD value
gold60+Gold spot price (XAU/USD)
pm_crypto_signals6,300+99 Polymarket crypto-related markets with YES probabilities
cross_metrics60+Combined snapshot: all prices, funding, OI, PM sentiment, BTC/Gold ratio, ETH/BTC ratio

Coverage

  • —Date range: 2026-03-23 to present (auto-updating)
  • —Crypto pairs: BTCUSDT, ETHUSDT, SOLUSDT
  • —Granularity: 15-minute candles, 8-hour funding, hourly OI
  • —Source: Binance Futures API, Gold-API, Polymarket Gamma API

Unique Value

No other dataset combines:

  1. 1.Crypto price data (candles, funding, OI) with
  2. 2.Prediction market probabilities (99 crypto markets on Polymarket) and
  3. 3.Gold spot prices for macro regime analysis

This enables research on:

  • —Do prediction markets lead crypto prices?
  • —Funding rate → price direction signals
  • —BTC/Gold ratio regime shifts
  • —Cross-asset sentiment analysis

Usage

python
import pandas as pd

candles = pd.read_parquet("candles.parquet")
funding = pd.read_parquet("funding_rates.parquet")
pm = pd.read_parquet("pm_crypto_signals.parquet")
metrics = pd.read_parquet("cross_metrics.parquet")

# BTC price vs PM crypto sentiment
btc = candles[candles.symbol == "BTC"].set_index("ts")
print(f"BTC range: ${btc.close.min():.0f} - ${btc.close.max():.0f}")
print(f"PM crypto markets tracked: {pm.market_id.nunique()}")

Full Dataset

This is a free sample (30 days). For the full historical dataset with 9.5M+ Polymarket prices across 9,550 markets:

  • —[Gumroad — $9 full dataset](https://manja8.gumroad.com/l/agyjd)
  • —[Gumroad — $29/mo live subscription](https://manja8.gumroad.com/l/luneql)

Auto-Updated

This dataset refreshes weekly via automated pipeline. Star the repo to get notifications.

Contact

LuciferForge@proton.me | @gmanjuu