Token OHLCV Minute

One Parquet table, token_ohlcv_1m, with 20 columns: the minute, the token and its chain, USD open, high, low and close, USD and token volume, and four supply figures including market cap. About 2.8 million rows for 100 tokens over 30 days.

About 2.8 million rows and roughly 180 MB as Parquet, estimated from a one-day sample of 84,629 rows at 65 bytes per row. The export for the 30 days to 8 October 2026 returned 2,777,302 rows; exact figures arrive with the delivery manifest.

The top 100 by the same rule as our hourly and daily token candles. Tokens are ranked by their median market cap over the 30 days, counting only bars where the circulating supply is known, with a floor of $100,000 traded in the window. The list is fixed when your order is built and shipped inside the files, so you can see which tokens you got.

30 days. The price index keeps 30 days of bars, so that is the longest window this dataset can hold. Each order is built once, when you buy, and covers the 30 days up to the latest full UTC day. It is a fixed set of files and does not update afterwards. For prices that keep updating, use the Bitquery Trading API.

Bars come from the Bitquery price index, the same series the Trading API serves. It prices each token in USD from its DEX trades and filters out MEV trades. Each row is one 1-minute bar from that index. We ran the export for two windows before release. Both returned every day in the window, and no bar had a high below its low.

Tokens outside the top 100, and trades on centralized exchanges, which are not in the source. Minutes with no trade have no row; nothing is forward filled. A full grid would be 4.3 million rows, so about a third of token minutes had no trade.

Market cap belongs to the currency, so every copy of a coin carries the same figure: WETH on Base shows the market cap of ETH. Never sum it across rows. Circulating supply is known for about 4,800 tokens; for the rest the market cap column equals the fully diluted value. Symbols and names repeat, so join on Token_Id. Prices are 32-bit floats in the source, good to about seven digits. Supply figures can be 0 on a bar where the reference data had not arrived.

Not as a fixed-price dataset. Wider token lists and longer ranges are quoted as custom scopes; use the enterprise form.

One line in DuckDB: SELECT * FROM read_parquet('token_ohlcv_1m/*.parquet') LIMIT 10. Pandas works too: pandas.read_parquet(path).

Each order is built once, when you buy, and covers the 30 days up to the latest full UTC day. It is a fixed set of files and does not update afterwards. For prices that keep updating, use the Bitquery Trading API. Delivered as Parquet with ZSTD compression, one file per day, under a stable layout with a JSON manifest listing every file and its sha256. We prepare the files within 7 business days of purchase and email you the location. You choose at checkout where to collect them: a Requester Pays S3 bucket, downloaded with your own AWS account, or a Cloudflare R2 bucket with a read-only API key and no egress fees. Files stay available for 7 days from delivery.