s-research / iexviz

demo of working with iex TOPS and DEEP messages

iexviz

This is demo of working with IEX message data. Most of the documenation and narrative is in the notebook/iex_viz directory.

This is a notebook to show how to work with IEX historic TOP and DEEPs data. The data downloads compresed as a large (3-4GB) pcap file, which a raw TCPIP format. This format is basically a stream of tcp messages, where IP headers have to be parsed from each message.

• First you need to parse the IEX pcap file.

  • if using python:, try IEX_hist_parser from IEXTools
  • if using java: try IEX Trading 4j-Hist
  • I recommend and will show using go. Secifically using the pcap2json and pcap2csv parsers from the go IEX project

• Pandas has issues reading the json, so I then used spark and python's json2parquet to convert json to parquet. Spark worked great on the TOPS.json, but required a debug config I didn't feel like fixing. So I resorted to python's json2parquet, which worked surprisingly well.

• This is also hosted on S3 as s3://iexviz if you want to work with some of the datasets

  • Overview notebook on S3
  • Processed TOPS parquet
  • Processed DEEP parquet
  • Intermediate TOPs JSON
  • Intermediate DEEP csv
  • Intermediate TOPs csv
  • Intermedieate DEEP json

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data               <- Note the gitrepo ignores the actual data files. You'll need to generate them or get from s3.
│   ├── external       <- Data from third party sources. - note used
│   ├── interim        <- Intermediate data that has been transformed. Used to house jsons and csv 
│   ├── processed      <- The final, canonical data sets for modeling. Location for processed parquet files. 
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py <- code decribes how to use go and js2on2parquet to process data.
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience