fastitch
NASDAQ TotalView-ITCH 5.0 → Polars, pandas, Arrow or Parquet. At native speed, in one line.
import fastitch
frames = fastitch.read("01302019.NASDAQ_ITCH50.gz", types="orders", symbols=["AAPL", "NVDA"])
frames["add_order"] # a Polars DataFrame, one row per Add Order message
NASDAQ publishes full-depth, message-by-message order book data as TotalView-ITCH 5.0 files: a few gigabytes per day, hundreds of millions of binary messages. Existing Python parsers hand you one Python object per message, which is fine for a demo and painful for a trading day.
fastitch decodes straight into columnar Arrow buffers in C++, so a whole day becomes ordinary DataFrames you can filter, group and join, and it never builds a Python object per message.
| Task (5M messages, 164 MB) | fastitch | itchcpp (C++) | itchfeed (Python) | meatpy (Python) |
|---|---|---|---|---|
| Add orders → pandas DataFrame | 0.59 s | 21.8 s (37x slower) | 30.0 s (51x slower) | n/a |
| Decode every message | 0.75 s | 1.9 s (2.5x slower) | 7.2 s (9.6x slower) | 10.4 s (14x slower) |
Synthetic session from fastitch.synthetic, 2-vCPU Linux VM, Python 3.13, best of 3, timings include imports.
fastitch's "decode" also builds Arrow tables for all 23 types; the others only create message objects.
Reproduce with python benchmarks/bench.py; numbers on real NASDAQ days welcome in an issue.
Install
pip install fastitch # prebuilt wheels for Linux, macOS, Windows · Python 3.10–3.13
pip install "fastitch[all]" # + polars, pandas, and ISA-L for faster .gz reading
What you get
- All 23 ITCH 5.0 message types, every field, named and typed (
add_order,order_executed,noii, …) - DataFrames out: Polars (default), pandas or a zero-copy Arrow table
- Filters pushed into C++: by symbol, by time of day (
start="09:30", end="16:00"), by message type or group ("orders","trades","auction","admin") - Order-book reconstruction: top-N levels, emitted on every change or sampled on a time grid
- Constant-memory streaming:
iter_batches()andto_parquet()handle full days on a laptop - Real timestamps: the trading date is read from NASDAQ file names, so you get
datetime[ns], not "nanoseconds since midnight" - Reads
.gzdirectly, plus raw bytes and file objects - A CLI for when you don't want to open Python at all
- Synthetic sessions built in, so you can try everything without a 5 GB download
Usage
Decode messages
import fastitch
# Everything (one DataFrame per message type)
frames = fastitch.read("01302019.NASDAQ_ITCH50.gz")
# Only what you need: filtering happens in C++ before anything reaches Python
fills = fastitch.read(
"01302019.NASDAQ_ITCH50.gz",
types=["order_executed", "order_executed_with_price", "trade"],
symbols=["AAPL", "MSFT"],
start="09:30", end="16:00",
)
# A single type, as pandas
adds = fastitch.read_frame("01302019.NASDAQ_ITCH50.gz", "add_order", backend="pandas")
types accepts codes ("A"), names ("add_order") or groups: orders, trades, auction, admin, all.
Prices are floats in dollars (pass raw_prices=True for the exact integer ticks).
Rebuild the order book
book = fastitch.book("01302019.NASDAQ_ITCH50.gz", "AAPL", depth=5, every="100ms")
every=None (the default) emits a row each time the top depth levels change; a duration such as
"1s" or "100ms" samples on a regular grid. The book is always built from the open, so a
start/end window never desyncs it. Correctness is tested row-for-row against an independent
pure-Python reference implementation.
Full days, constant memory
# Stream to one Parquet file per message type
fastitch.to_parquet("01302019.NASDAQ_ITCH50.gz", "parquet/", types="orders")
# Or process batch by batch
for batch in fastitch.iter_batches("01302019.NASDAQ_ITCH50.gz", "trades", batch_rows=5_000_000):
handle(batch["order_executed"])
Command line
fastitch info 01302019.NASDAQ_ITCH50.gz # message counts by type
fastitch convert 01302019.NASDAQ_ITCH50.gz -o parquet/ -t orders -s AAPL MSFT
fastitch book 01302019.NASDAQ_ITCH50.gz -s AAPL -d 10 -e 1s -o aapl_book.parquet
fastitch sample demo.itch -n 1000000 # synthetic data to play with
No data yet?
from fastitch.synthetic import generate
data = generate(1_000_000, symbols=["AAPL", "MSFT"]) # a self-consistent ITCH session, as bytes
fastitch.read(data)
Real sample days are published by NASDAQ at https://emi.nasdaq.com/ITCH/Nasdaq%20ITCH/.
See examples/quickstart.py for spreads, imbalance and volume bars in a dozen lines.
How it works
_spec.pyholds the byte layout of every ITCH 5.0 message: the single source of truth, checked against the spec's message lengths at import.- The C++ core is layout-driven: it frames the length-prefixed stream (messages may straddle read chunks), applies symbol/time filters, and appends each field to a contiguous native-endian column. The GIL is released while parsing.
- Python wraps those columns as Arrow arrays without copying them again, then hands them to Polars or pandas.
- The order book keeps live orders in a hash map and price levels in sorted maps per symbol, applying adds, executions, cancels, deletes and replaces exactly as the spec defines.
Benchmarks
Run them yourself: python benchmarks/bench.py (synthetic) or python benchmarks/bench.py <ITCH file>.
Each tool runs in its own process; best of three.
Contributing
Issues and PRs welcome. Good first contributions: MoldUDP64 packet captures as input, more book analytics (queue position, order flow imbalance), and a Rust or DuckDB front end.
pip install -e ".[test]" && pytest
License
MIT. ITCH is a trademark of Nasdaq, Inc.; this project is not affiliated with or endorsed by Nasdaq.
1 maintainer
Metadata
1 maintainer
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Transparency logSecurity for fastitch 0.1.0
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