launchtape

Python access to Launch Tape: daily files of Solana launch data, one file per dataset per UTC day, as gzipped JSON lines (.jsonl.gz) and as Parquet (.parquet, the same rows as typed columns).

A free 2-hour sample of every dataset is on the site; launchtape downloads it, checks its SHA-256 and reads it.

Install

pip install launchtape              # needs only requests
pip install "launchtape[pandas]"    # adds pandas for launchtape.frame()
pip install "launchtape[parquet]"   # adds pyarrow, to read the Parquet files

Python 3.9 or newer.

Quickstart

Crash rate of pump.fun graduates, split by "instant" launches (graduated within 5 s of creation):

import launchtape as lt

path = lt.sample("pregrad")                                    # free sample, SHA-256 checked, cached
rec = lt.frame(path, types="record")[["mint", "sym", "instant", "ageS"]]
out = lt.frame(path, types="outcome")[["mint", "crashed", "maxGain"]]
df = rec.merge(out, on="mint")                                 # one row per graduate seen in both
print(df.groupby("instant")["crashed"].agg(["mean", "count"]))
print(lt.schema("pregrad"))                                    # every row type and field

The sample is two hours, so the counts are small: it shows the shape of the data, not a result.

API

for row in lt.load("gradtrades", types="grad", limit=5):      # streams, so a 5-million-row day is fine
    print(row["mint"], row["capSol"])

path = lt.fetch("https://launchtape.io/dl/<token>", "pregrad", "2026-10-11", dest="data/")
pq = lt.fetch("https://launchtape.io/dl/<token>", "gradtrades", "2026-10-11", dest="data/", format="parquet")

Parquet

Every day file and sample also comes as Parquet: the same rows, checked against the .jsonl.gz before it ships, as one table whose columns are every field of every row type (null where a row does not have the field). t stays integer milliseconds, nested objects are structs and arrays lists, padfeed's usd is a map keyed by quote mint, and pregrad's curve, src and cross (text in some row types, an object or a number in others) are text columns, an object or number as its JSON text. Read it with pandas directly, or through lt.load / lt.frame (needs pyarrow):

import pandas as pd
df = pd.read_parquet(lt.sample("pregrad", format="parquet"))   # loads in a fraction of the JSON time
cols = ["type", "bt", "mint", "side", "user", "quoteAmount"]
trades = pd.read_parquet("data/gradtrades-2026-10-11.parquet", columns=cols)   # only the columns you need

Times: t is milliseconds since 1970 UTC (pd.to_datetime(df["t"], unit="ms", utc=True)). Coverage gaps are rows in the files (down, up, resume, end), never filled in: see each schema.

A full gradtrades day is about 5 million rows; for a DataFrame, pass types= and/or limit=, or stream with load(). A download that does not match its SHA-256 raises launchtape.ChecksumError and is not kept. Environment: LAUNCHTAPE_CACHE changes the cache folder.

Command line

launchtape datasets
launchtape sample gradtrades                  # prints the cached path
launchtape sample pregrad --format parquet    # the Parquet copy of the sample
launchtape head gradtrades -n 5               # first rows of the sample, as JSON lines
launchtape head pregrad -t record -n 2        # only rows of one type
launchtape schema pregrad
launchtape fetch https://launchtape.io/dl/<token> pregrad 2026-10-11 -o data/
launchtape fetch https://launchtape.io/dl/<token> pregrad 2026-10-11 -o data/ --format parquet

python -m launchtape ... works too.

Full days: prices and how to buy

• Starter, $10 a month: all 4 datasets, each new day's files as the UTC day closes, plus the last 7 days on file.
• Full history, $49 a month: everything in Starter, plus every day on file back to October 3 2026.

Buy at launchtape.io: pick a plan, enter your email, press Pay (USDC or SOL on Solana; your wallet opens with everything filled in). The private download link (https://launchtape.io/dl/<token>) arrives by email, and launchtape fetch reads it.

Licences

• This package (the code): MIT, see LICENSE.
• The data it downloads, the free samples included, has its own licence: personal and research use inside your own team. No resale, republishing or redistribution of the files, in whole or in part. Results you derive from it (charts, papers, models, strategies) are yours. Full text: launchtape.io.

The data is market data recorded from public sources, provided as is. It is not financial advice.