{ "info": { "author": "Colin Swaney", "author_email": "colin-swaney@uiowa.edu", "bugtrack_url": null, "classifiers": [], "description": "Copyright (c) 2016 Colin Swaney\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\nDescription: # hfttools\n A Python toolkit for high-frequency trade research.\n \n Website: [https://cswaney.github.io/hfttools/](https://cswaney.github.io/hfttools/)\n \n ## What?\n HFT Tools is a Python toolkit for financial researchers. \"But I don't use Python!\" No worries--all will be explained (keep reading).\n \n ## Why?\n 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!\n \n ## Sure. But what does is actually do?\n 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. \n \n ## Installation\n ![alt text](https://img.shields.io/pypi/v/hfttools.svg \"pypi\")\n \n If you're a Python user, simply install using the Python package manager:\n \n ```\n pip install hfttools\n ```\n \n ## Basic Usage\n \n To create a new HDF5 database from an ITCH data file `itch_010113`:\n \n ```python\n import hfttools as hft\n \n hft.unpack(fin='itch_010113.bin',\n ver=4.1,\n date='2013-01-01',\n fout='itch.hdf5'\n nlevels=10,\n names=['GOOG', 'AAPL'],\n method='hdf5')\n ```\n \n 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`:\n \n ```python\n hft.read(db='itch.hdf5',\n date='2013-01-01',\n names='GOOG')\n ```\n \n For more information, see our tutorial at the projects [webpage](https://www.google.com).\n \n ## Tips\n \n Create massive datasets quickly by running multiple jobs. 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