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Macro Sentiment Indicators

Pyth oracle prices and Polymarket prediction market odds

Category
States
Point-in-time snapshots
Granularity
~1 second (Pyth), per-trade (Polymarket)
Format
Apache Parquet
Snappy compression, Hive-partitioned
Nodes
1 (EU-Central)
Frankfurt — low-latency to HyperLiquid & Ethereum

About This Dataset

Sub-second oracle price feeds from Pyth Network and per-trade prediction market odds from Polymarket. Cross-referenced macro sentiment for crypto and world events.

Partitions
source: pyth, polymarket

Schema

ColumnTypeDescription
time_chaintimestampSource timestamp (UTC)
time_localtimestampIngestion timestamp (UTC)
sourcestringData source (pyth or polymarket)
symbolstringInstrument/market identifier
pricefloat64Price or probability
confidencefloat64Confidence interval (Pyth) or volume (Polymarket)

Use Cases

R2 Path

s3://algotick-data-lake/states/macro_sentiment/source={source}/year=YYYY/month=MM/day=DD/node={node}/data.parquet
Replace YYYY, MM, DD with the target date. Data is collected from node=eu-central.

Query with DuckDB

import duckdb

df = duckdb.sql("""
    SELECT *
    FROM read_parquet(
        's3://algotick-data-lake/states/macro_sentiment/source={source}/year=2026/month=04/day=20/node=eu-central/data.parquet'
    )
    LIMIT 100
""").df()

print(f"Rows: {len(df)}, Columns: {list(df.columns)}")

Download via API

import requests

resp = requests.get(
    "https://algotick.dev/v2/history/raw?dataset=macro_sentiment&source={source}&date=2026-04-20",
    stream=True,
)

with open("macro-sentiment.parquet", "wb") as f:
    for chunk in resp.iter_content(8192):
        f.write(chunk)

# Then query locally
import duckdb
df = duckdb.sql("SELECT * FROM 'macro-sentiment.parquet' LIMIT 100").df()
print(df)
Get API Key →

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