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Datasets:
rogerdehe
/
mktdata-lighter-2026

Tasks:
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1B - 10B
Tags:
crypto
cryptocurrency
finance
orderbook
order-book
l2
Libraries:
Datasets
Dask
Polars
License:
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action
uint8
side
uint8
price_raw
int64
price_prec
uint8
size_raw
uint64
size_prec
uint8
order_id
uint64
flags
uint8
sequence
uint64
ts_event
int64
ts_init
int64
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17,139,955,964
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128
17,139,955,964
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1
3,119
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17,139,956,039
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3,123
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937,131
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0
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17,139,956,039
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3,124
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1,056,198
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17,139,956,039
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3,120
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17,139,956,077
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1,784,323,892,007,791,400
End of preview. Expand in Data Studio

LIGHTER USDT Perpetual — L2 Order-Book Deltas + Trades (2026)

Raw market-microstructure tick data for LIGHTER USDT-margined perpetual futures: full-depth L2 order-book deltas and trades, as zstd-compressed Parquet. Recorded by the goldmine market-data recorder; converted losslessly from its internal GMKT v1 format.

This repo holds year 2026. Data is sharded one repo per exchange per year (mktdata-lighter-<year>) to keep per-repo file counts and size bounded.

⚠️ Under construction / growing — this is a live recording, extended over time.

What's here

  • Deltas = incremental order-book updates (add / update / delete / clear).
  • Trades = individual executions.
  • Snapshots are inside the delta stream: every ~60s a full-book snapshot is spliced in as a burst of delta rows flagged F_SNAPSHOT. There is no separate snapshot table — filter flags & 32 to find them.

Layout 

{SYMBOL}/{YYYY-MM}/{SYMBOL}_{YYYYMMDD}_deltas.parquet
{SYMBOL}/{YYYY-MM}/{SYMBOL}_{YYYYMMDD}_trades.parquet
{SYMBOL}/{YYYY-MM}/{SYMBOL}_{YYYYMMDD}_control.jsonl   (only when gaps/reconnects occurred)

One {SYMBOL} folder per instrument; one file per UTC day.

Two HF subsets (viewer dropdown / load_dataset config): orderbook = the *_deltas.parquet files, trades = the *_trades.parquet files.

Schema

deltas.parquet — one row per order-book update:

column type meaning
action uint8 1=add · 2=update · 3=delete · 4/5=clear
side uint8 1=bid · 2=ask
price_raw / price_prec int64 / uint8 fixed-point; price = price_raw · 10^-price_prec
size_raw / size_prec uint64 / uint8 fixed-point; size = size_raw · 10^-size_prec
order_id uint64
flags uint8 bit 5 (value 32) = F_SNAPSHOT → row belongs to a periodic full-book snapshot burst
sequence uint64
ts_event / ts_init int64 exchange event time / local receive time, UTC nanoseconds

trades.parquet — one row per trade: price_raw/price_prec, size_raw/size_prec, aggressor (1=buy 2=sell), trade_id (string), ts_event, ts_init (UTC ns).

Price/size are stored as lossless fixed-point (raw + prec). Reconstruct a real value with raw * 10.0 ** -prec.

Usage 

# whole subset via datasets
from datasets import load_dataset
ob = load_dataset("rogerdehe/mktdata-lighter-2026", "orderbook")   # or "trades"

# or a single file
from huggingface_hub import hf_hub_download
import pandas as pd
p = hf_hub_download("rogerdehe/mktdata-lighter-2026",
                    "BTCUSDT-PERP/2026-07/BTCUSDT-PERP_20260716_deltas.parquet",
                    repo_type="dataset")
df = pd.read_parquet(p)
df["price"] = df["price_raw"] * 10.0 ** -df["price_prec"]
snapshots = df[df["flags"] & 32 != 0]           # periodic full-book snapshots

Reconstruct the book at any time t: take the last snapshot burst before t (a CLEAR flagged F_SNAPSHOT, then its ADD rows) and replay deltas up to t.

Notes

  • Timestamps are UTC nanoseconds. The dataset viewer works via the two configs above (orderbook / trades); you can also load files directly.
  • Data provided as-is for research; no warranty of accuracy or completeness.
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