A Python toolkit for high-frequency trade research.
HFT Tools is a Python toolkit for financial researchers. It is designed to make data collection simple.
The goal of this project is to provide a common, open-source tool for market microstucture research using NASDAQ HistoricalView-ITCH data. Don't pay for data!
HFT Tools creates scalable, research-ready databases from NASDAQ HistoricalView-ITCH data files. These data files are provided "as is" in a compressed, binary format that is not particularlyl useful. HFT Tools decodes these files and creates tables containing the time series of messages as well as the time series of reconstructed order books.
This package runs on Python3.5. You will also need the following to create databases (these are not installed automatically):
After you have installed and configured these, simply install using the Python package manager. We recommend using a virtual environment:
virtualenv -p python3 venv source venv/bin/activate pip install hfttools
To create a new HDF5 database from an ITCH data file
import hfttools as hft hft.unpack(fin='itch_010113.bin', ver=4.1, date='2013-01-01', fout='itch.hdf5' nlevels=10, names=['GOOG', 'AAPL'], method='hdf5')
This will create a file
itch.hdf5 containing message and order book data for Google and Apple. To read the order book data back into your Python session, use
hft.read(db='itch.hdf5', date='2013-01-01', names='GOOG')
For more information, see our tutorial at the projects webpage.
Create massive datasets quickly by running jobs simultaneously (e.g. on your university's cluster). All databases support simultaneous read/write.
This package is released under an MIT license. Please cite me (e.g. HFTTools (Version 0.0.2, 2016)).
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