ML in Finance
Public Speak + Daily report, University of Cambridge, CUATS, 2024
This is a blog I will update my journey in Quantative Finance and hopefully it will also inspire people to join.
10 Jan
- Working with high-frequency market data
Traditional download：
How to work with NASDAQ order book data （How trades are communicated: The FIX protocol）
AlgoSeek minute bars: Equity quote and trade data
API access：
Data sources：
Quandl docs and Python API yfinance Quantopian Zipline LOBSTER The Investor Exchange IEX Cloud financial data infrastructure Money.net Trading Economic Barchart Alpha Vantage Alpha Trading Labs Tiingo stock market tools
Industry News： Bloomberg and Reuters lose data share to smaller rivals, FT, 2018
- How to work with Fundamental data
Financial statement data
Automated processing using XBRL markup
Other fundamental data sources： Compilation of macro resources by the Yale Law School Capital IQ Compustat MSCI Barra Northfield Information Services Quantitative Services Group
Financial Data Storage
In particular, we compare the following:
CSV: Comma-separated, standard flat text file format.
HDF5: Hierarchical data format, developed initially at the National Center for Supercomputing, is a fast and scalable storage format for numerical data, available in pandas using the PyTables library.
Parquet: A binary, columnar storage format, part of the Apache Hadoop ecosystem, that provides efficient data compression and encoding and has been developed by Cloudera and Twitter. It is available for pandas through the pyarrow library, led by Wes McKinney, the original author of pandas.